Generally speaking, PCQA methods can be divided into full-reference PCQA (FR-PCQA), reduced-reference (RR-PCQA), and no-reference PCQA (NR-PCQA) methods according to the involvement extent of the reference point clouds. However, the pristine reference point clouds are not always available in many practical situations, thus NR-PCQA has a wider range of applications. Hence, we focus on the NR-PCQA in this paper. Reviewing the development of NR-PCQA, NR-PCQA metrics are either based on the point cloud features extracted by statistical models [1]} and end-to-end neural networks [2]} or based on the projected image features obtained via hand-crafted manners [3]}, [4]}, [5]} or 2D convolutional neural networks (CNN) [6]}, [7]}. Such methods fail to jointly make use of the information from 3D point clouds along with 2D projections, thus resulting in unsatisfactory performance.
